{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "49fff408-3fe1-4a53-8fb9-8906e55549d4",
   "metadata": {},
   "source": [
    "### 本文按以下步骤分析\n",
    "1. 背景\n",
    "2. 提出问题\n",
    "3. 理解数据\n",
    "4. 数据清洗\n",
    "5. 可视化分析\n",
    "6. 结论与建议\n",
    "  \n",
    "#### 背景  \n",
    "通过对比主要城市的生产总值、产业规模、零售业情况、进出口情况、就业、教育、医疗、房地产开发，可分析一个城市的经济发展水平和民生水平。\n",
    "#### 提出问题\n",
    "目标需求1：对比2015年至2017年主要城市年度数据的变化趋势  \n",
    "目标需求2：对比各个城市在这几年的国内生产总值情况，对比不同城市的贡献程度和发展趋势？  \n",
    "目标需求3：对比各个城市第一、二、三产业在这几年的发展状况是怎样的？  \n",
    "目标需求4：对比各个城市平均在职工资、学校资源、卫生医院设备情况，看看哪些城市工资高、教学资源丰富、医疗保障水平是怎样的？  \n",
    "目标需求5：对比分析哪些城市是房地产投资重地呢？  \n",
    "#### 理解数据\n",
    "    area 地区\t\n",
    "    year 年份\n",
    "    total_value 国内生产总值\n",
    "    one_value 第一产业增加值\t\n",
    "    twol_value 第二产业增加值\t\n",
    "    three_value 第三产业增加值\t\n",
    "    zero_value 社会商品零售总额\t\n",
    "    inout 货物进出口总额\t\n",
    "    population 年末总人口\t\n",
    "    pay 在岗职工平均工资\t\n",
    "    student  普通高等学校在校学生数\t\n",
    "    hospital 医院、卫生院数\t\n",
    "    realty 房地产开发投资额\n",
    "    estate_ratio 新增列,用于分析房地产在国内生产总值的占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "43a6fff1-bb8d-421d-bf4d-04f3f897729e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.globals import ThemeType\n",
    "from pyecharts.charts import Bar,Line,Pie,Timeline\n",
    "\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "large = 22\n",
    "med = 16\n",
    "small = 12\n",
    "params = {\n",
    "    'font.sans-serif':['SimHei'],\n",
    "    'figure.figsize':(16,12),\n",
    "    'figure.titlesize':large,\n",
    "    'axes.unicode_minus':False,\n",
    "    'axes.labelsize':med,\n",
    "    'axes.titlesize':large,\n",
    "    'xtick.labelsize':med,\n",
    "    'ytick.labelsize':med,\n",
    "    'legend.fontsize':med,\n",
    "}\n",
    "plt.rcParams.update(params)\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "sns.set_color_codes()\n",
    "sns.set(font_scale=med)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9b2cc69b-4506-4493-9567-2c60a252b89b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入数据\n",
    "df2015 = pd.read_csv(r'原始数据/2015年国内主要城市年度数据.csv')\n",
    "df2016 = pd.read_csv(r'原始数据/2016年国内主要城市年度数据.csv')\n",
    "df2017 = pd.read_csv(r'原始数据/2017年国内主要城市年度数据.csv')\n",
    "data = df2015.append(df2016).append(df2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "65c6f480",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 108 entries, 0 to 35\n",
      "Data columns (total 14 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   地区            108 non-null    object \n",
      " 1   年份            108 non-null    int64  \n",
      " 2   国内生产总值        108 non-null    float64\n",
      " 3   第一产业增加值       108 non-null    float64\n",
      " 4   第二产业增加值       108 non-null    float64\n",
      " 5   第三产业增加值       108 non-null    float64\n",
      " 6   社会商品零售总额      108 non-null    float64\n",
      " 7   货物进出口总额       108 non-null    float64\n",
      " 8   年末总人口         108 non-null    float64\n",
      " 9   在岗职工平均工资      108 non-null    int64  \n",
      " 10  普通高等学校在校学生数   108 non-null    float64\n",
      " 11  医院、卫生院数       108 non-null    int64  \n",
      " 12  房地产开发投资额      105 non-null    float64\n",
      " 13  estate_ratio  105 non-null    float64\n",
      "dtypes: float64(10), int64(3), object(1)\n",
      "memory usage: 12.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be354ae5",
   "metadata": {},
   "source": [
    "——仅房地产开发投资额有三个空值，查看后发现是同一个城市的缺失数据，不影响其他城市和其他列的数据分析，故不作处理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c41476ea-0fdc-426d-a4c5-05297e8cb7f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# timeline图 --条形图:x轴城市，y轴各列数据，以年份作为timeline，加上产业占比饼图\n",
    "def timeline_plot(data):\n",
    "    years = sorted(data['年份'].unique())\n",
    "    timeline = Timeline(init_opts=opts.InitOpts(theme=ThemeType.DARK))\n",
    "    timeline.add_schema(is_auto_play=True)\n",
    "    for year in years:\n",
    "        timeline.add(bar_pie_peryear(data,year),str(year))\n",
    "    return timeline\n",
    "    \n",
    "def bar_pie_peryear(data,year):\n",
    "    data = data[data['年份'] == year]    \n",
    "    cols = ['社会商品零售总额','货物进出口总额','年末总人口','在岗职工平均工资','普通高等学校在校学生数','医院、卫生院数','房地产开发投资额']\n",
    "    bar = (\n",
    "        Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK))\n",
    "        .add_xaxis(xaxis_data=data['地区'].tolist())\n",
    "        .add_yaxis(\n",
    "            series_name='国内生产总值',\n",
    "            y_axis=data['国内生产总值'].tolist(),\n",
    "            label_opts=opts.LabelOpts(is_show=False)\n",
    "        )\n",
    "        .add_yaxis(\n",
    "            series_name='第一产业增加值',\n",
    "            y_axis=data['第一产业增加值'].tolist(),\n",
    "            label_opts=opts.LabelOpts(is_show=False)\n",
    "        )\n",
    "        .add_yaxis(\n",
    "            series_name='第二产业增加值',\n",
    "            y_axis=data['第二产业增加值'].tolist(),\n",
    "            label_opts=opts.LabelOpts(is_show=False)\n",
    "        )\n",
    "        .add_yaxis(\n",
    "            series_name='第三产业增加值',\n",
    "            y_axis=data['第三产业增加值'].tolist(),\n",
    "            label_opts=opts.LabelOpts(is_show=False)\n",
    "        )\n",
    "        .set_global_opts(\n",
    "            title_opts=opts.TitleOpts(title='{}年年度数据'.format(year)),\n",
    "            datazoom_opts=opts.DataZoomOpts(type_='inside'),\n",
    "            xaxis_opts=opts.AxisOpts(\n",
    "                axispointer_opts=opts.AxisPointerOpts(is_show=True,type_='shadow')\n",
    "            ),\n",
    "            legend_opts=opts.LegendOpts(pos_left='15%',pos_top='5%')\n",
    "        )\n",
    "    )\n",
    "    for col in cols:\n",
    "        bar.add_yaxis(\n",
    "            series_name=col,\n",
    "            y_axis=data[col].tolist(),\n",
    "            is_selected=False,\n",
    "            label_opts=opts.LabelOpts(is_show=False)\n",
    "        )\n",
    "    pie = (\n",
    "        Pie(init_opts=opts.InitOpts(theme=ThemeType.DARK))\n",
    "        .add(\n",
    "            series_name='{}年产业占比'.format(year),\n",
    "            data_pair=[\n",
    "                ['第一产业增加值',data[ '第一产业增加值'].sum()],\n",
    "                ['第二产业增加值',data[ '第二产业增加值'].sum()],\n",
    "                ['第三产业增加值',data[ '第三产业增加值'].sum()],\n",
    "            ],\n",
    "            center=['85%','30%'],\n",
    "            radius='90px',\n",
    "            label_opts=opts.LabelOpts(formatter='{b}:\\n{d}%',position='inside'),\n",
    "            tooltip_opts=opts.TooltipOpts(formatter='{b}: {c}')\n",
    "        )\n",
    "    )\n",
    "    return bar.overlap(pie)\n",
    "\n",
    "# 条形图 --x轴城市，y轴各年份的列数据，hue年份\n",
    "def bar_percity(data):\n",
    "    years = sorted(data['年份'].unique())\n",
    "    bar = (\n",
    "        Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK,width='1500px',height='800px'))\n",
    "        .add_xaxis(xaxis_data=sorted(data['地区'].unique().tolist()))\n",
    "        .set_global_opts(\n",
    "            title_opts=opts.TitleOpts(title='各地区不同特征对比图'),\n",
    "            datazoom_opts=opts.DataZoomOpts(type_='inside'),\n",
    "            xaxis_opts=opts.AxisOpts(\n",
    "                axispointer_opts=opts.AxisPointerOpts(is_show=True,type_='shadow'),\n",
    "            ),\n",
    "            legend_opts=opts.LegendOpts(pos_left='90%',pos_top='5%',orient='vertical',type_='scroll',padding=1,item_width=10,item_height=10)\n",
    "        )\n",
    "    )\n",
    "    cols = ['第一产业增加值', '第二产业增加值', '第三产业增加值', '社会商品零售总额','货物进出口总额', '年末总人口', '在岗职工平均工资', '普通高等学校在校学生数', '医院、卫生院数', '房地产开发投资额']\n",
    "    for year in years:\n",
    "        bar.add_yaxis(\n",
    "            series_name='{}年{}'.format(year,'国内生产总值'),\n",
    "            y_axis=data[data['年份'] == year].sort_values(by='地区')['国内生产总值'].tolist(),\n",
    "            is_selected=True,\n",
    "            label_opts=opts.LabelOpts(is_show=False)\n",
    "        )\n",
    "    for year in years:\n",
    "        for col in cols:\n",
    "            bar.add_yaxis(\n",
    "                series_name='{}年{}'.format(year,col),\n",
    "                y_axis=data[data['年份'] == year].sort_values(by='地区')[col].tolist(),\n",
    "                is_selected=False,\n",
    "                label_opts=opts.LabelOpts(is_show=False)\n",
    "            )\n",
    "    return bar\n",
    "\n",
    "# 折线图 --2015-2017年房地产占国内生产总值比例;比例=[房地产列]/[国内生产总值列]\n",
    "data['estate_ratio']  = np.around(data['房地产开发投资额'] / data['国内生产总值'],4)\n",
    "def estate_ratio_line(data):\n",
    "    cities = data['地区'].unique()\n",
    "    years = sorted(data['年份'].unique().tolist())\n",
    "    all_ratio = []\n",
    "    for year in years:\n",
    "        data_ = (data[data['年份'] == year]['房地产开发投资额'].sum() / data[data['年份'] == year]['国内生产总值'].sum()).round(4).item()\n",
    "        all_ratio.append(data_)\n",
    "    line = (\n",
    "        Line(init_opts=opts.InitOpts(theme=ThemeType.DARK,width='1000px'))\n",
    "        .add_xaxis(xaxis_data=[str(y) for y in years])  # x轴为category类,所以转换为string类型\n",
    "        .set_global_opts(\n",
    "            title_opts=opts.TitleOpts(title='各地区每年的房地产开发投资额占生产总值的比例'),\n",
    "            legend_opts=opts.LegendOpts(pos_left='90%',pos_top='5%',orient='vertical',type_='scroll'),\n",
    "            xaxis_opts=opts.AxisOpts(\n",
    "                axispointer_opts=opts.AxisPointerOpts(is_show=True,type_='line')\n",
    "            )\n",
    "        )\n",
    "    )\n",
    "    line.add_yaxis(\n",
    "        series_name='总比例',\n",
    "        y_axis=all_ratio,\n",
    "        is_selected=True\n",
    "    )\n",
    "    for city in cities:\n",
    "        data_region = data[data['地区'] == city].sort_values(by='年份')['estate_ratio'].tolist()\n",
    "        line.add_yaxis(\n",
    "            series_name=city,\n",
    "            y_axis=data_region,\n",
    "            is_selected=False\n",
    "        )\n",
    "    return line"
   ]
  },
  {
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   "execution_count": 9,
   "id": "5b1209e9-c2f2-4c50-a74f-2e988e52bcaf",
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       "                    \"gridIndex\": 0,\n",
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       "                    \"offset\": 0,\n",
       "                    \"splitNumber\": 5,\n",
       "                    \"minInterval\": 0,\n",
       "                    \"splitLine\": {\n",
       "                        \"show\": false,\n",
       "                        \"lineStyle\": {\n",
       "                            \"show\": true,\n",
       "                            \"width\": 1,\n",
       "                            \"opacity\": 1,\n",
       "                            \"curveness\": 0,\n",
       "                            \"type\": \"solid\"\n",
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       "                }\n",
       "            ],\n",
       "            \"title\": [\n",
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       "                    \"text\": \"2017\\u5e74\\u5e74\\u5ea6\\u6570\\u636e\",\n",
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       "            \"tooltip\": {\n",
       "                \"show\": true,\n",
       "                \"trigger\": \"item\",\n",
       "                \"triggerOn\": \"mousemove|click\",\n",
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       "                \"showContent\": true,\n",
       "                \"alwaysShowContent\": false,\n",
       "                \"showDelay\": 0,\n",
       "                \"hideDelay\": 100,\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 14\n",
       "                },\n",
       "                \"borderWidth\": 0,\n",
       "                \"padding\": 5\n",
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       "    ]\n",
       "};\n",
       "                chart_c3ca57c0ea5744c2a4dc84004968d96a.setOption(option_c3ca57c0ea5744c2a4dc84004968d96a);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x54f35f8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对比各年年度数据\n",
    "timeline = timeline_plot(data)\n",
    "timeline.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c33c8b5f",
   "metadata": {},
   "source": [
    "从全国总体来看，第三产业占三大产业的一半以上，且有年年增长的趋势；第一、二产业都有下降的趋势。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9778bc2-e9ec-4273-9d48-d51c7efb3248",
   "metadata": {},
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    "**在国内生产指标中**  \n",
    "从总体上看，除了乌鲁木齐、呼和浩特、大连、沈阳这几个城市有所波动，其他城市的生产总值都在持续增长，其中北京和上海领先与其他城市；天津、广州、深圳、重庆在第二梯队；而拉萨、海口、西宁、银川倒数。  \n",
    "  \n",
    "**在三大产业指标中**  \n",
    "重庆的第一产业领先于城市；上海、天津、深圳、重庆的第二产业是第一梯队；北京和上海的第三产业领先于其他城市。  \n",
    "  \n",
    "**在社会商品零售总额指标中**  \n",
    "北京、上海、广州、重庆处于第一梯队，而且北京和上海势头很猛。  \n",
    "  \n",
    "**在货物进出口指标中**  \n",
    "北京、上海、深圳遥遥领先于其他城市。  \n",
    "  \n",
    "**在总人口指标中**  \n",
    "重庆人口远远多于其他城市。  \n",
    "  \n",
    "**在职工平均工资指标中**  \n",
    "上海、北京、拉萨处于第一梯队，其他城市不相上下。  \n",
    "  \n",
    "**在校学生数量指标中**  \n",
    "乌鲁木齐、厦门、宁波、拉萨、海口、深圳、西宁、银川落后于其他城市。  \n",
    "  \n",
    "**在医院、卫生院数量指标中**\n",
    "重庆在2015-2016年在这个指标上一枝独秀，但2017年回落严重，降低到与北京、成都同一水平。  \n",
    "  \n",
    "**在房地产开发投资额指标中**  \n",
    "北京、上海、重庆领先，北京有下降的趋势。  \n",
    "\n",
    "**综上所述：**  \n",
    "发展较好城市有：北京、上海、广州、深圳、重庆。  \n",
    "发展比较落后的城市有：拉萨、海口、西宁、银川。"
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     "execution_count": 13,
     "metadata": {},
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    "line = estate_ratio_line(data)\n",
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   "metadata": {},
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    "**对比分析发展较好的五个城市的房地产开发情况**  \n",
    "只有重庆是高于平均水平，但有下降的态势；广州和深圳都有上升的趋势；上海这几年持平；北京直线下降。"
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